The second category is the posterior sampling [1]}, [2]}, which incorporates ideas from Bayesian learning and focuses on using posterior probabilities for more targeted exploration. The algorithm based on posterior sampling will modify its probability distribution after each sampling. Through a large number of samples, the variance of each action will be reduced.
